So far, most existing methods are proposed to deal with image classification tasks. Only few works about noisy labels are applied to saliency detection [1]}, anomaly detection [2]} or instance segmentation [3]}. In this paper, we propose the first usage of learning with noisy labels for semi-supervised semantic segmentation task, which can be considered as a pixel-wise classification problem. However, relations between the pixel labels need to be adequately modeled, and very few studies have explicitly addressed this with unreliable and noisy labels. Inspired by the previous researches, we propose a graph based label noise detection and correction framework and attempt to learn with noisy labels in pixel-level segmentation task.
